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检索条件"任意字段=Conference on Visual Information Processing IV"

共 97 条记录，以下是1-10 订阅

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Extracting Cognitive Workload Factors on Modelling Attention Resources using Ocular information in Figure Search Task 28

Extracting Cognitive Workload Factors on Modelling Attention...

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28th International conference on information visualisation (iv)

作者： Nakayama, Minoru Okano, Tomomi Tokyo Inst Technol Informat & Commun Engn Tokyo Japan

ISBN: (纸本)9798350380170;9798350380163

The possibility of measuring temporal change in cognitive workload during tasks is examined using microsaccade (MS) rates and pupillary change. The experimental task was designed as a search for a specific figure, where the task difficulty and reaction accuracy were controlled during the trials. Individual cognitive workload was measured after the experimental sessions were completed, using ratings for NASA-TLX scale. Since the source may be a common one, changes in latent attention resources required for the task were estimated using a designated state-space model, using the observation data in order to synthesise measurement of MS rates and pupillary change. The predicted levels of attention resources correspond to the activity during the performance of the experimental tasks during the trials, and reflected some of the rating scores for workload scales. Also, the ranges of confidence intervals for attention resources correlate significantly with the ratings for information processing at the stage where visual stimulus is presented during tasks.

关键词： cognitive load microsaccade rate pupil change modelling chronological analysis

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A Machine Learning Pipeline Using KNIME to Predict Hospital Admission in the MIMIC-iv Database 14

A Machine Learning Pipeline Using KNIME to Predict Hospital ...

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14th International conference on information, Intelligence, Systems and Applications, IISA 2023

作者： Tsoni, Rozita Kaldis, Vasileios Kapogianni, Ioanna Sakagianni, Aikaterini Feretzakis, Georgios Verykios, Vassilios S. Hellenic Open University Patra Greece Kat General Hospital of Attika Emergency Department Marousi Greece Intensive Care Unit Sismanogleio General Hospital Marousi Greece

ISBN: (纸本)9798350318067

It is well known that overcrowding in emergency department (ED) lowers the standard of care and raises the risk of medical errors. An initial predictive supplementary tool of hospital admission at an early stage of a patient's arrival to the emergency department (ED) can provide health care professionals a number of advantages, such as, more efficient patient flow management and better hospital care. In this paper, we use data from the Medical information Mart for Intensive Care iv Emergency Department (MIMIC-iv-ED) database to predict whether a patient will be admitted to the hospital or not. The choice of predictive attributes was driven by simplicity (a set of basic vital signs were used) so that the prediction can be made at an early stage of the patient's arrival. Several versions of Machine Learning (ML) algorithms based on Decision Trees (DT) were used for classification and prediction. An important asset of the proposed methodology is that the whole process is implemented through an ML pipeline created with an open-source, visual programming tool. The proposed methodology contains the pre-processing stage, the modelling stage includes seven classifiers, and the combined visualization of the evaluation of the predictive models. The Gradient Boosted Trees method outperforms the rest of the algorithms that were used. An accuracy of 80% can be achieved only by using early triage data. © 2023 IEEE.

关键词： Forecasting

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Using Polymorphic Glyphs to Support the visual Exploration of Hierarchical Spatio-Temporal Data 19th

Using Polymorphic Glyphs to Support the Visual Exploration o...

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19th International-Federation-for-information-processing-Technical-Committee-13 (IFIP TC13) International conference on Human-Computer Interaction (INTERACT)

作者： Menin, Aline Ayari, Hadil Michel, Franck Winckler, Marco Univ Cote Azur I3S INRIA SPARKS Wimm Team Paris France

ISBN: (纸本)9783031422928;9783031422935

Studying spatio-temporal data is essential to understand the processes of the real world. However, the design of effective visualizations to explore spatio-temporal data is not a straightforward task due to the inherent multidimensional aspects of the data. In this paper, we explore the usage of polymorphous glyphs (i.e. glyphs that change shapes according to the context) to support the exploration of multiple hierarchical levels and dimensions of the data. We implemented our approach in the form of a web-based visualization interface that we demonstrate through a case study of the ISSA KG, which describes scientific publications in the field of agriculture, thus supporting domain experts on investigating where, when and how different crops are cultivated.

关键词： information visualization Polymorphous glyphs Linked data visualization Spatio-temporal visualization

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Synthesis of model features for fake news detection using large language models 8

Synthesis of model features for fake news detection using la...

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8th International conference on Computational Linguistics and Intelligent Systems. Volume iv: Computational Linguistics Workshop, CLW-CoLInS 2024

作者： Wierzbicki, Adam Shupta, Andrii Barmak, Olexander Polish-Japanese Academy of Information Technology Koszykowa 86 str. 02-008 Warsaw Poland Khmelnytskyi National University 11 Instytuts’ka str. Khmelnytskyi29016 Ukraine

In recent years, the issue of fake news has become exceedingly pertinent due to its rapid dissemination through social media and online platforms. Detecting fake news requires the utilization of various methods and resources. This work proposes an approach to building a model for fake news detection using generative artificial intelligence and natural language processing. The main focus of the proposed approach is the synthesis of model features for fake news detection using generative artificial intelligence and Natural Language processing. The use of this proposed approach not only facilitates the detection of fake news but also renders the detection process transparent and customizable by the user. Additionally, through the proposed method, users gain the ability to expand features and train the system to adapt to new types of fake news and their variations. Experimental results, presented both qualitatively through visual analytics and quantitatively through statistical indicators, convincingly demonstrate the effectiveness of the proposed approach in detecting fake news with satisfactory accuracy levels reaching 90% and provide users with sufficient interpretability of the obtained decisions. Overall, this research aims to create an approach for detecting fake news that may have a significant impact on addressing this issue in contemporary society. © 2024 Copyright for this paper by its authors.

关键词： Feature extraction

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Extracting Cognitive Workload Factors on Modelling Attention Resources Using Ocular information in Figure Search Task

Extracting Cognitive Workload Factors on Modelling Attention...

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International conference on information visualisation (iv)

作者： Minoru Nakayama Tomomi Okano Information and Communications Engineering Tokyo Institute of Technology Meguro Tokyo Japan

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

关键词： visualization Analytical models Accuracy information processing State-space methods Data mining Load modeling

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visual Question Answering of Remote Sensing Image Based on Attention Mechanism 5th

Visual Question Answering of Remote Sensing Image Based on...

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5th IFIP TC 12 International conference on Intelligence Science, ICIS 2022

作者： Zhang, Shihuai Wei, Qiang Li, Yangyang Chen, Yanqiao Jiao, Licheng The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation Collaborative Innovation Center of Quantum Information of Shaanxi Province School of Artificial Intelligence Xidian University Xi’an710071 China The Key Laboratory of Aerospace Information Applications The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050081 China

ISBN: (纸本)9783031149023

In recent years, the research of attention mechanism has made significant progress in the field of computer vision. In the processing of visual problems of remote sensing images, the attention mechanism can make the computer focus on important image areas and improve the accuracy of question answering. Our research focuses on the role of synergistic attention mechanisms in the interaction of question representations and visual representations. On the basis of Modular Collaborative Attention (MCA), according to the complementary characteristics of global features and local features, the hybrid connection strategy is used to perceive global features at the same time without weakening the attention distribution of local features. The impact of attention mechanisms on various types of visual question answering questions has been evaluated:(i) scene classification (ii)object comparison (iii) quantitative statistics (iv) relational judgment. By fusing the global features and local features of different modalities, the model can obtain more information between modalities. Model performance evaluation under the RSVQA-LR dataset. Experimental results show, the method in this paper improves the global accuracy by 9.81% than RSVQA. © 2022, IFIP International Federation for information processing.

关键词： Image enhancement

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Preface iv 2024

Preface IV 2024

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International conference on information visualisation (iv)

作者： Ebad Banissi

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

All aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of our information-dependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities. Artificial Intelligence and its enabling constructs, such as information visualization, Analytics, and Machine and Deep Learning, influence our daily lives. All these are tightly tied to raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scaling to knowledge acquisition, leading to new visual knowledge discovery. The domain and its evolutionary nature steer the next generation of research activities. processing the relationship between these phases has added new impetus to understanding and communicating them, from raw data to knowledge. The tradition of communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to knowledge discoveries and hypothesis analysis. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the processes, from raw data to the knowledge acquisition stage. But there is a new twist: fast-developing generative AI with ever-increasing access to data outsmarts humans in decision-making. It is a new evolutionary step in the human journey, no doubt - with it lies ahead of us its impacts and its pitfalls.

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Octree-Based Temporal-Spatial Context Entropy Model for LiDAR Point Cloud Compression

Octree-Based Temporal-Spatial Context Entropy Model for LiDA...

引用

2023 IEEE International conference on visual Communications and Image processing, VCIP 2023

作者： Sun, Longhua Wang, Jin Shi, Yunhui Zhu, Qing Yin, Baocai Ling, Nam Beijing University of Technology Faculty of Information Technology Beijing China Santa Clara University Department of Computer Engineering California United States

ISBN: (纸本)9798350359855

It's difficult to effectively remove redundancy in Li-DAR point clouds due to their extremely sparse and nonuniform distribution. Taking advantage of both octree-based methods and voxel-based schemes, we propose to design an effective temporal-spatial context to compress the sequence octree-structured point cloud data into a more compact bitstream. In this paper, we first build a temporal-spatial multiscale context for the deep learning entropy model. It further utilize the correlation of sequential point cloud data from both the spatial domain and temporal domain. In terms of spatial context, we design a hierarchical dependency in an octree to encode the occupancy information of each non-leaf octree node into a bitstream. We propose to further group the nodes according to their octant which effectively expands the context receptive field. In terms of temporal context, the KNN algorithm is applied to explore the most relative context with the strongest dependency in the temporal domain. Finally, we design a voxel re-localization network to convert the discrete voxels into refined 3D points, which makes up for the coordinate loss in the process of generating an octree. The quantitative evaluation shows that our method outperforms state-of-The-Art baselines with saving most bitrate on KITTI Odometry dataset, and achieving the best reconstreuction benefit by the designed refinement module. © 2023 IEEE.

关键词： Optical radar

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A Machine Learning Pipeline Using KNIME to Predict Hospital Admission in the MIMIC-iv Database

A Machine Learning Pipeline Using KNIME to Predict Hospital ...

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International conference on information, Intelligence, Systems and Applications (IISA)

作者： Rozita Tsoni Vasileios Kaldis Ioanna Kapogianni Aikaterini Sakagianni Georgios Feretzakis Vassilios S. Verykios Hellenic Open University Patra Greece Emergency department KAT General Hospital of Attika Marousi Greece Intensive Care Unit Sismanogleio General Hospital Marousi Greece

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ConVisQA: A Natural Language Interface for visually Exploring Online Conversations 24

ConVisQA: A Natural Language Interface for Visually Explorin...

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24th International conference information visualisation (iv)

作者： Siddiqui, Nadia Hoque, Enamul York Univ Sch Informat Technol Toronto ON Canada

ISBN: (纸本)9781728191348

There has been an exponential growth of asynchronous online conversations thanks to the rise of social media. Analyzing and gaining insights from such conversations can be quite challenging for a user, especially when the discussion becomes very long. Traditional sites present a conversation in a paginated list view, making it very difficult to find comments of interests about a specific topic and/or opinions which may be scattered around a long thread of discussion. In this paper, we introduce a natural language interface that supports the user to quickly locate and browse through the comments that are relevant to her information needs. Our system takes a question asked by the reader about a conversation as input and then automatically finds the answer using natural language processing techniques. It then presents the results by highlighting in a visual interface, enabling the user to quickly navigate through the comments that match her information needs. Our case studies with three users suggest that the system can help the user to effectively fulfill her information needs by highlighting the relevant comments to their question.

关键词： Online Conversations Natural Language Interface information visualization Text Analysis Parser Context Free Grammar

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